Search

In recent years, the increase in the use of services in cloud, fog, edge, and IoT ecosystems has been very notable. On the one hand, environmental sustainability is affected by this type of ecosystem since it can produce a large amount of energy consumption which translates into CO2 emissions into the atmosphere. On the other hand, due to the COVID-19 pandemic, the use of these ecosystems has increased considerably. Thus, it is necessary to apply policies and techniques to maximize sustainability within these ecosystems. Some of these policies and techniques are those based on artificial intelligence. However, the current processing of these policies and techniques can also consume a lot of resources. From this perspective, this article aims to clarify whether the sustainability of cloud/fog/edge/IoT ecosystems is improved by the application of artificial intelligence. To do this, a systematic literature review is developed in this paper. In addition, a set of classifications of the analyzed works is proposed based on the different aspects related to these ecosystems, their sustainability, and the applicability of artificial intelligence to improve them. • The use of cloud, fog, edge, and IoT has increased drastically in the last few years. • It is necessary to apply policies to maximize the sustainability of AI. • This work clarifies whether the sustainability of these environments is improved by AI. • A SLR is performed to achieve this aim. • The application of AI to these environments is a newborn topic. [ABSTRACT FROM AUTHOR]

High carbon emissions and population aging are two obstacles to China's development as an emerging economy. It is urgent to scientifically examine the impacts of population aging on carbon emissions to discover new pathways for urban carbon emission reduction. However, existing studies face challenges in terms of focusing on and methodology for accurately capturing the role of industrial production and energy consumption in the environmental effects of population aging. This paper constructs a chain mediation analytical framework and systematically examines the intrinsic linkages between population aging and carbon emissions in Chinese cities from 2000 to 2020 using a two-way fixed effects model. This paper has three main findings. (1) The positive correlation between urban population aging and carbon emissions development is generally low, with coupling coordination degree values of 0.4233, 0.4458, 0.4220, 0.4715, and 0.4665 for each yearly cross-section. (2) For every 1% increase in the population aging rate, carbon emissions decrease by 0.3478% on average. The carbon reduction effect of population aging is significantly greater in low-emissions cities and high-income cities than in other cities. (3) The industrial upgrading (IU) path, energy conservation (EC) path and chain mediation (CM) path account for 72.43%, 7.23% and 20.34%, respectively, of the indirect effects. If the causal link between IU and EC is not considered, the EC effect will be overestimated by 281.16%. The results of the study suggest that properly coping with population aging and reducing carbon emissions are not completely opposed to each other, a conclusion that passes both endogeneity exclusion and robustness check. This paper advocates replacing the one-size-fits-all approach in carbon emissions management and investing more in policy preferences to support carbon emission reduction in both high-emissions and low-income cities. [Display omitted] •The synergistic trends of carbon emissions and population aging are not apparent. •Population aging significantly reduces carbon emissions at the city scale. •Linkage effect of industrial upgrading and energy conservation on carbon emission reduction. •Energy conservation effect of population aging amplified by industrial upgrading. [ABSTRACT FROM AUTHOR]

In the global effort to reduce CO 2 emissions, the concurrent enhancement of pollutant degradation and reductions in fossil fuel consumption are pivotal aspects of microalgae-mediated wastewater treatment. Clarifying the degradation mechanisms of bacteria and microalgae during pollutant treatment, as well as regulatory biolipid production, could enhance process sustainability. The synergistic and inhibitory relationships between microalgae and bacteria are introduced in this paper. The different stimulators that can regulate microalgal biolipid accumulation are also reviewed. Wastewater treatment technologies that utilize microalgae and bacteria in laboratories and open ponds are described to outline their application in treating heavy metal-containing wastewater, animal husbandry wastewater, pharmaceutical wastewater, and textile dye wastewater. Finally, the major requirements to scale up the cascade utilization of biomass and energy recovery are summarized to improve the development of biological wastewater treatment. [Display omitted] • The synergistic and antagonistic interactions between bacteria and microalgae are summarized. • Different stimulators that could regulate microalgal biolipid accumulation are also reviewed. • Wastewater treatment technologies that utilize microalgae and bacteria in the laboratory and open ponds are described. • The trends in microalgae-bacterial consortium in multistage energy production are proposed. [ABSTRACT FROM AUTHOR]

The vigorous development and the increasing popularity of cloud computing highlight the necessity of reducing data center energy consumption and the environmental impact of carbon dioxide emissions. For geographically distributed data centers, cloud servers are connected to the conventional power grid and in addition they are supported by an attached renewable energy source. Since the carbon footprint rate of energy consumption has dynamic differences in space, reducing energy consumption does not mean decrease carbon emission, which indicates that energy consumption and carbon footprint need to be synergistically optimized. In this paper, an energy and carbon-aware algorithm for virtual machine placement is proposed. The goal is to obtain a virtual machine allocation scheme that aims to achieve the trade-off between energy consumption and carbon emissions by improving renewable energy utilization. The experimental results show that the proposed approach is more energy-efficient and greener, which can also maximize the renewable energy utilization with 73.11% while ensuring the SLA violation with 0.2% in comparison to the baseline algorithms. • Propose a novel energy-aware VM placement approach in cloud data centers. • Introduce renewable energy for managing energy consumption and carbon footprint. • Design renewable energy generation prediction method based on EEMD and TCN. • Demonstrate the superiority of the proposed algorithm with five baseline algorithms. [ABSTRACT FROM AUTHOR]

In today's world, where economic development and environmental sustainability are becoming increasingly important aspects of national strategy, attention to the impact of different economic sectors on climate change is becoming an integral part of scientific research. This article focuses on analyzing the impact of primary and secondary economic sectors development on carbon dioxide (CO 2) emissions at the sub-national level in Russia from 2005 to 2019. The aim of the study is to provide an in-depth understanding of the relationships between the dynamics of these sectors and CO 2 emission levels in different regions of the country. Weighted regression and panel data methods were applied to better identify the patterns of the impact. The results show that the size of population and electricity consumption have the highest impact on CO 2 emissions. So that, the expansion of nuclear and gas generation capacity, as well as significant improvement of energy efficiency, are of crucial importance to reduce the emissions. Other sectors have a heterogeneous impact and requires more differential approaches, considering the specifics of regions. Taking into account the significant differences between the Russian constituent entities, this paper emphasizes the low informativeness of assessments at the national level and their inadequacy in terms of improving the efficiency of domestic management, including decarbonization policies. • The impact of considered economic sectors on CO 2 emissions were analyzed in 80 Russian constituent entities in 2005–2019. • Weighted regression and panel data methods were used to catch the specifics of Federal districts and constituent entities. • Population size and electricity consumption have the highest impact on CO 2 emissions. • Increasing energy efficiency and modernization of energy facilities are the most promising solutions to reduce the emissions. [ABSTRACT FROM AUTHOR]

This paper constructs a carbon quota allocation index that takes into account equity, efficiency and ecological construction, and calculates carbon emissions and energy consumption data in important periods based on the expected carbon emission targets and economic and social development indicators of the Chinese government. Based on the calculated carbon emissions, the zero-sum game data Envelopment model (ZSG-DEA) is used to discuss the initial allocation of regional quotas and the optimal carbon quota scheme. The results show that:(1) there is a large gap between the optimal carbon quota and the initial carbon quota allocation in Shandong, Guangdong, Jiangsu and other provinces in 2025, and the implementation of emission reduction measures should be accelerated. (2) By 2030, the final allocation of Beijing, Tianjin, Inner Mongolia, Qinghai, Shanghai, Ningxia, Liaoning and Xinjiang will be positive. The provinces with negative final allocation should carry out the work of carbon peak as soon as possible to avoid increasing the pressure of emission reduction in the future. (3) The central region faces greater pressure of emission reduction, while the western region can accept the transfer of carbon emissions from other regions over time. The research conclusions have important policy implications for establishing a fair and effective carbon quota allocation mechanism, achieving the national total carbon emission control target, stimulating the vitality of the unified carbon market, and promoting regional coordinated emission reduction. • There exist differences in carbon quota allocation among provinces in the future. • There are changes in the initial and optimal carbon quotas obtained by each province. • The role of carbon emissions between regions is changing over time. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies. [Display omitted] • Enrich the view of renewable energy's indirect impact on carbon emissions. • Renewable energy's carbon emission reductions go beyond replacing fossil fuels. • Constructed a mediating effect model between renewable energy and carbon emissions. • Renewable energy can promote or inhibit carbon emissions through various pathways. • Research helps develop more effective renewable energy sustainability strategies. [ABSTRACT FROM AUTHOR]

Due to high material density, high energy consumption density and CO 2 emission density, it is not only difficult but significant to clarify the relationship between energy consumption, the CO 2 emission and the production cost in different conditions. However, the previous researches rarely refer how to balance the energy consumption, the CO 2 emission and the production cost after the fluctuation of material, energy and carbon price as well as what will happen to them if production structure changes. Therefore, based on the conservation law of mass and energy, to study iron and steel manufacturing process (ISMP), this paper, taking carbon price into consideration, establishes a muti-optimization model of energy consumption, CO 2 emission and cost. After optimization with different objectives, the production cost per tonne of crude steel is reduced by 192.03 CNY (7.71%), the CO 2 emission per tonne of crude steel is reduced by 224.22 kg (13.37%), and the energy consumption per tonne of steel is reduced by 51.20 kgce (9.10%). Moreover, based on the optimization results under different objectives, it is ironmaking process (coal ratio and ore ratio) and steelmaking process (amount of scrap steel) that has more impact on three above as well as ore blending and coal blending have a great influence on production cost but little effect on energy consumption and CO 2 emission. • The muti-objective optimization model of iron and steel industry is established. • The carbon price is considered during model establishment and analyzing. • The objective with energy consumption, CO 2 emissions and cost are optimized. • The factors affecting cost, energy consumption and CO 2 emission are discussed. [ABSTRACT FROM AUTHOR]

The paper presents energy and carbon footprints of sewage treatment plants (STPs) operating at different scales and using different technology options based on primary data from 50 STPs operating in India and the UK. The study used a combination of fundamental mass-balance approach for energy consumption and the methodology defined by IPCC for the carbon emissions. Small-scale institutional STPs consume twelve times the energy consumed by large-scale municipal STPs, the corresponding energy intensities being 4.87 kWh/m 3 and 0.40 kWh/m 3 respectively. Embodied energy from construction material and chemicals accounted for 46% and 33% of the total energy intensity of the municipal and institutional STPs respectively. The average carbon footprint of large-scale STPs is 0.78 kgCO 2 eq/m 3 and for small-scale STPs it is 3.04 kgCO 2 eq/m 3 . However, fugitive emissions from large-scale STPs constituted 74% of the total carbon emissions whereas the figure was only 0.05% for small-scale STPs. Average electrical energy intensity in STPs in India is much lower (0.14 kWh/m 3 ) than that in the UK (0.46 kWh/m 3 ). This is due to the reason that STPs in India do not have resource recovery processes and use solar heat for sludge drying. The paper offers information and insights for designing low carbon strategies for urban waste infrastructure. [ABSTRACT FROM AUTHOR]

Identifying key drivers of manufacturing CO 2 emissions is critical to carbon reduction practices. For manufacturing, CO 2 emissions are mainly determined by production capacity and production scale. However, traditional production-theoretical decomposition analysis (PDA) fails to consider production-scale-related drivers. To better support policy development and implementation, this paper improves PDA based on industrial linkage theory. The improved model can identify seven production-capacity-related drivers and five production-scale-related drivers, allowing a comprehensive understanding of CO 2 emission drivers. Then this model is implemented to investigate CO 2 emission changes in 18 manufacturing sectors in Hubei Province, China, from 2012 to 2017. Results show that manufacturing CO 2 reduction efforts in Hubei Province have yielded some achievements, with reduced potential energy intensity and improved CO 2 emission technical efficiency in most sectors. Changes in external market demand and final demand structure have contributed to CO 2 reduction in most sectors. Results also reveal some problems in manufacturing in Hubei Province, such as the inability to improve CO 2 emission technical efficiency and CO 2 emission technology strength, the slow improvement of energy utilization technical efficiency and energy utilization technology strength, and the reduction of value-added rate. • A new decomposition analysis model is built to decompose CO 2 emission changes. • The new model integrates industrial linkage theory into PDA. • Seven of twelve drivers relate to production capacity and five to production scale. • CO 2 emission changes in 18 manufacturing sectors in Hubei Province are decomposed. • Several important implications for manufacturing CO 2 reduction are proposed. [ABSTRACT FROM AUTHOR]

To achieve sustainable development, local governments are under enormous pressure to simultaneously achieve the three assessment tasks (TATs) of energy saving, CO₂ reduction, and pollution reduction. However, the TATs are often managed by different authorities and have three types of measures (TTMs) that correspond to them. The lack of adequate cooperation between these authorities has led to the inefficient investment of policy resources and even to policy conflicts, and the interactions among the TTMs are not yet known. To this end, this paper uses the MCEE model to assess the interactions among the TTMs quantitatively using Guangzhou, China, as a case study. The results showed the following. (1) According to the current development trend, if the authorities managing the TATs continue to work alone, they will not be able to fulfill the corresponding assessment tasks in the future. (2) The TTMs have interactions with each other, and their synergies are far greater than their trade-offs. From 2015 to 2035, it is expected that energy-saving measures can accomplish 54.39% of the CO₂ reduction tasks and 32.74% of the pollution reduction tasks indirectly, and low-carbon measures can accomplish 55.53% of the energy saving tasks and 27.20% of the pollution reduction tasks indirectly, However, environmental-protection measures will cause fewer trade-offs (energy demand and CO 2 emissions increase by 3.76% and 2.88%, respectively). (3) In some years, the contribution of interactions among the TTMs are even higher than their direct contribution to the TATs. Our findings suggest that intensive cooperation between authorities is necessary, and that the benefits of such cooperation will outweigh the disadvantages. • An integrated energy-economic-environmental assessment model for multiple indicators is established. • An index evaluation method for multiple synergies and trade-offs is established. • It reveals three types of measures do have interactions and their synergies are far greater than the trade-offs. • Intensive cooperation among authorities is necessary and the benefits of such cooperation will outweigh the disadvantages. [ABSTRACT FROM AUTHOR]

After the Paris Climate Conference (COP21), many countries start progressing towards carbon neutrality targets. In doing so, green technology innovations (GTIs) and clean energy are the essential factors that can help to achieve the carbon neutrality goal. Therefore, this paper examines the linkages between green technology innovation and renewable energy and carbon dioxide emissions based on the STIRPAT model in Turkey during the time of 1990–2018. The study used testing like "unit-root" to verify the variables' integrative properties containing the information for structural breaks. Also, the bootstrapping ARDL-bound testing technique is used to analyze the relationship between the variables. The causal relationship between green technology innovation, energy consumption, renewable energy, population, income per capita, and carbon dioxide emissions is tested through a Granger causality test. The empirical findings show that green technology innovation, renewable energy, energy consumption, population, income per capita, and carbon dioxide emissions are co-integrated for the long-term association. Additionally, green technology innovation and renewable energy decline carbon dioxide emissions, whereas energy consumption, population, and per capita enhance carbon emissions. This paper helps the policymakers design a comprehensive policy for strengthening environmental sustainability through green technology innovation and renewable energy, specifically in the region of Turkey. • Examine the impact of GTIs and renewable energy on CO 2 emissions in Turkey. • We employed bootstrapping ARDL-bound testing technique. • GTIs and renewable energy are lowering CO 2 emissions. • Non-renewable energy consumption boosts CO2 emissions. • Population and personal income are also determinantal to carbon emissions. [ABSTRACT FROM AUTHOR]

Protecting our environment while maintaining economic growth, requires a delicate balance among interlinked sustainable development policies. In this paper, we examine China's economic industries, including a high-resolution of the country's electricity sector during 2020–2030, using a multi-objective optimization model based on Input-Output analysis. This model, investigates the synergy and trade-offs of sustainable development goals in maximizing employment and GDP while minimizing energy and water consumption, CO 2 emissions, and five major pollutants to advance a sustainable industrial structure adjustment pathway for China. Our results reveal that there exists both synergies and trade-offs among multiple objectives, e.g., synergy among goals of minimizing air pollutant emissions and trade-offs between minimizing energy consumption and maximizing employment. Through the planned industrial restructuring period (2020–2030), the GDP, employment, carbon emission, and energy consumption will increase respectively by, 96.1%, 7.2%, 16.8%, 16.8%, and 6.3%, while pollutant emissions would decrease. Moreover, our research indicates that energy and water conservation should be prioritized in industrial structure adjustment strategies and policies. Our model demonstrates how the synergies and trade-offs among multiple policy targets can empower policy-makers, especially in developing nations, to make more informed and optimized industrial structure adjustment policies for sustainable development. • A multi-objective optimization model based on the Input-Output model is proposed. • Synergies and trade-offs among policy targets are examined. • The structural adjustment of economic sectors with fine-resolution power sectors is investigated. • The compromise solution and the most recommended policy priority are explored. [ABSTRACT FROM AUTHOR]

Globally, all countries have producing different levels of carbon emissions and also facing both the problems of climate change and global warming due high carbon emissions in the atmosphere. Therefore, it is important to cutting carbon emissions in the atmosphere. This is only possible by switching to cleaner fuels, use of innovation technologies and development of carbon capture storages. These can substantially help the nations to reaching carbon neutrality. Given this background, t his paper examines the effect of disaggregated energy consumption, technological innovations, capital on economic output and CO 2 emissions in India for the period of 1990–2018. Based on empirical analysis, our long-run elasticities indicate that disaggregated energy consumption and technological innovations have a positive impact on economic growth, while renewable energy consumption and technological innovations have a positive impact on CO 2 emissions. It implies that more use of energy consumption producing significant amount of CO 2 emissions and by using renewable energy consumption and technological innovations (i.e. carbon capture storages) can significantly lowering CO 2 emissions, which is clearly indicating that India has moving towards carbon neutrality. The causality analysis further indicates a unidirectional causal relationship running from disaggregated energy usage to economic growth and carbon emissions. These empirical findings suggest that the increased consumption of renewable power does not lead to rise carbon emissions, which, in turn, ensures sustainable economic growth. • Examines the disaggregated energy consumption, technological innovations, capital on economic output and CO 2 emissions. • Time series data from 1990 to 2018 was used to analyze the empirical results. • Disaggregated energy consumption and technological innovations have a positive impact on economic growth. • Renewable energy consumption and technological innovations have a positive impact on CO 2 emissions. • Suggest that increased in consumption of renewable power does not lead to rise carbon emissions. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the causal relationship among renewable energy technologies, biomass energy consumption, per capita GDP, and CO 2 emissions for Germany. We constructed an innovative algorithm, the Quantum model, and applied it with Machine Learning experiments – through a software capable of emulating a quantum system – to data over the period of 1990–2018. This process is possible after eliminating the "irreversibility" of classical computations (unitary transformations) by making the process "reversible". The empirical findings support the powerful role of biomass energy in reducing carbon dioxide emissions, although the effect of renewable energy technology displays a much stronger magnitude. Moreover, income remains an important determinant of environmental pollution in Germany. • An innovative Machine Learning algorithm (the Quantum model) has been constructed and applied. • The empirical findings support the powerful role of biomass energy in reducing CO 2 emissions. • GDP represents an important determinant of environmental pollution in Germany. [ABSTRACT FROM AUTHOR]

Considering the enlarging inter-provincial disparities in China as regards carbon emissions and carbon intensity (carbon emissions per unit gross domestic product), this paper is the first study to investigate the inter-provincial carbon imbalance by constructing and employing the Kaya-Zenga index. We use China's panel data of provincial-level carbon emissions over 1995–2016 to quantitatively measure the levels of inter-provincial imbalance and polarization in carbon emissions and carbon intensity. Further, we decompose the Kaya-Zenga index into different contributing factors both regionally and structurally and perform a scenario analysis to identify the corresponding regionally differentiated countermeasures regarding carbon emission reduction. The results show that the imbalance in carbon emissions is mainly caused by imbalances in population scale and income level, while the imbalance in carbon intensity predominantly results from imbalances in energy efficiency and energy mix. In addition, for heavy manufacturing provinces, the respective emission-reduction strategy should aim at lowering energy intensity through local technology improvement and inter-regional technology transfer. For light manufacturing and high technology provinces, carbon emission reduction is harder to be achieved; however, a mix of policies of improving energy efficiency, optimizing energy mix, and industrial upgrading should be implemented. The results of the scenario analysis indicate that reducing imbalance in carbon intensity under different scenarios can lead to a substantial reduction in carbon emissions (up to 10%). • We investigate the inter-provincial carbon imbalance within China. • We construct the Kaya-Zenga index and decompose the index into four contributors. • We use a panel data set of 30 provincial-level regions in China over 1995–2016. • Carbon imbalance is mainly caused by imbalances in population and energy efficiency. • Reducing intra-group imbalance in carbon intensity can abate carbon emissions. [ABSTRACT FROM AUTHOR]